Linear and nonlinear regression with stable errors

John P. Nolan and Diana Ojeda-Revah

Journal of Econometrics, 2013, vol. 172, issue 2, 186-194

Abstract: In this paper we describe methods and evaluate programs for linear regression by maximum likelihood when the errors have a heavy tailed stable distribution. The asymptotic Fisher information matrix for both the regression coefficients and the error distribution parameters are derived, giving large sample confidence intervals for all parameters. Simulated examples are shown where the errors are stably distributed and also where the errors are heavy tailed but are not stable, as well as a real example using financial data. The results are then extended to nonlinear models and to non-homogeneous error terms.

Keywords: Heavy tailed regression; Stable distributions; Score function (search for similar items in EconPapers)
JEL-codes: C13 C16 C51 G17 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:172:y:2013:i:2:p:186-194

DOI: 10.1016/j.jeconom.2012.08.008

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